Biology subjects

Nesvizhskii, A. I.

Publications and source records attributed to Nesvizhskii, A. I..

36 records · Page 2Linked to original sources

bioRxiv2022

HLA-Glyco: A large-scale interrogation of the glycosylated immunopeptidome

MHC-associated peptides (MAPs) bearing post-translational modifications (PTMs) have raised intriguing questions regarding their attractiveness for targeted therapies. Here, we developed a novel computational glyco-immunopeptidomics workflow that integrates the ultrafast glycopeptide search of MSFragger with a glycopeptide-focused false discovery rate (FDR) control. We performed a harmonized analysis of 8 large-scale publicly available studies and found that glycosylated MAPs are predominantly presented by the MHC class II. We created HLA-Glyco, a resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. Our comprehensive resource reveals high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between classical HLA class II allele groups. To support the nascent field of glyco-immunopeptidomics, we include the optimized workflow in the FragPipe suite and provide HLA-Glyco as a free web resource.

immunology↗

bioRxiv2022

One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. We have integrated MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data dependent acquisition (DDA), or both data types combined. We compared MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, and in silico library-based DIA-NN and MaxDIA. We demonstrated the fast and sensitive performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

bioinformatics↗

bioRxiv2022

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

bioinformatics↗

bioRxiv2022

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Post-translational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry (MS)-based proteomics, largely relying on traditional database search methods to annotate resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem MS experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new Labile Mode in the MSFragger search engine that can tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum annotation rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

bioinformatics↗

bioRxiv2022

Mining for ions: diagnostic feature detection in MS/MS spectra of post-translationally modified peptides

Post-translational modifications (PTMs) are an area of great interest in proteomics, with a surge in methods to detect them in recent years. However, PTMs can introduce complexity into proteomics searches by fragmenting in unexpected ways. Detecting post-translational modifications in mass spectrometry-based proteomics traditionally relies on identifying ions shifted by the masses of the modifications. This presents challenges for many PTMs. Labile PTMs lose part of their modification mass during fragmentation, rendering shifted fragment ions unidentifiable, and isobaric PTMs are indistinguishable by mass, requiring other diagnostic ions for disambiguation. Furthermore, even modifications that have undergone extensive characterization often produce different fragmentation patterns across instruments and conditions. To address these deficiencies and facilitate the next generation of PTM identification, we have developed a method to automatically find diagnostic spectral features for any PTM, allowing subsequent searches to take advantage of additional metrics and increase PTM identification and localization rates. The method has been incorporated into the open-search annotation tool PTM-Shepherd and the FragPipe computational platform.

bioinformatics↗

bioRxiv2022

Identification of secreted proteins by comparison of protein abundance in conditioned media and cell lysates.

Analysis of the full spectrum of secreted proteins in cell culture is complicated by leakage of intracellular proteins from damaged cells. To address this issue, we compared the abundance of individual proteins between the cell lysate and the conditioned medium, reasoning that secreted proteins should be relatively more abundant in the conditioned medium. Marked enrichment for signal-peptide-bearing proteins with increasing conditioned media to cell lysate ratio, as well loss of this signal following brefeldin A treatment, confirmed the sensitivity and specificity of this approach. The subset of proteins demonstrating increased conditioned media to cell lysate ratio in the presence of Brefeldin A identified candidates for unconventional secretion via a pathway independent of ER to Golgi trafficking.

biochemistry↗

bioRxiv2021

Multi-attribute Glycan Identification and FDR Control for Glycoproteomics

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward, however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collision or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B- (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with FDR control of both peptide and glycan components that is both sensitive and robust against false identifications.

bioinformatics↗

bioRxiv2021

GRASP55 regulates mutant huntingtin unconventional secretion and aggregation

Recent studies demonstrated that the Golgi stacking proteins, GRASPs, especially GRASP55, regulate Golgi-independent unconventional secretion, but the underlying mechanism remains unknown. Here, we used mutant huntingtin (Htt-Q74) as a model system to address this question. Our results demonstrate that Htt secretion is GRASP55- and autophagy-dependent, and is enhanced under stress conditions such as starvation and ER stress. Mechanistically, GRASP55 facilitates Htt secretion by tethering autophagosomes to lysosomes to promote autophagosome maturation and by stabilizing p23/TMED10, a channel for translocation of cytoplasmic proteins into the ERGIC lumen. Moreover, GRASP55 level is upregulated by various stresses to facilitate unconventional secretion, while inhibition of Htt-Q74 secretion by GRASP55 knockout enhances Htt aggregation and toxicity. Lastly, comprehensive secretomic analysis identified novel cargoes secreted by the same unconventional pathway, such as TAGLN, PAICS and PRDX1. This study provides important information on the role of GRASP55 in unconventional protein secretion and Huntingtons disease progression.

cell biology↗

bioRxiv2021

Kinesin-binding protein remodels the kinesin motor to prevent microtubule-binding

Kinesins are tightly regulated in space and time to control their activation in the absence of cargo-binding. Kinesin-binding protein (KIFBP) was recently discovered to bind the catalytic motor heads of 8 of the 45 known kinesin superfamily members and inhibit binding to microtubules. In humans, mutation of KIFBP gives rise to Goldberg-Shprintzen syndrome (GOSHS), but the kinesin(s) that is misregulated to produce clinical features of the disease is not known. Understanding the structural mechanism by which KIFBP selects its kinesin binding partners will be key to unlocking this knowledge. Using a combination of cryo-electron microscopy and crosslinking mass spectrometry, we determined structures of KIFBP alone and in complex with two mitotic kinesins, revealing regions of KIFBP that participate in complex formation. KIFBP adopts an alpha-helical solenoid structure composed of TPR repeats. We find that KIFBP uses a 2-pronged mechanism to remodel kinesin motors and block microtubule-binding. First, KIFBP engages the microtubule-binding interface and sterically blocks interaction with microtubules. Second, KIFBP induces allosteric conformational changes to the kinesin motor head that displace a key structural element in the kinesin motor head (-helix 4) required for microtubule binding. We identified two regions of KIFBP necessary for in vitro kinesin-binding as well as cellular regulation during mitosis. Taken together, this work establishes the mechanism of kinesin inhibition by KIFBP and provides the first example of motor domain remodeling as a means to abrogate kinesin activity.

cell biology↗

bioRxiv2021

Uncovering the modified immunopeptidome reveals insights into principles of PTM-driven antigenicity

Antigen processing and presentation are critical for modulating tumor-host interactions. While post-translational modifications (PTMs) can alter the binding and recognition of antigens, their identification remains challenging. Here we uncover the role PTMs may play in antigen presentation and recognition in human cancers by profiling 29 different PTM combinations in immunopeptidomics data from multiple clinical samples and cell lines. We established and validated an antigen discovery pipeline and showed that newly identified modified antigens from a murine cancer model are cancer-specific and can elicit T cell killing. Systematic analysis of PTMs across multiple cohorts defined new haplotype preferences and binding motifs in association with specific PTM types. By expanding the antigenic landscape with modifications, we uncover disease-specific targets, including thousands of novel cancer-specific antigens and reveal insight into PTM-driven antigenicity. Collectively, our findings highlight an immunomodulatory role for modified peptides presented on HLA I, which may have broad implications for T-cell mediated therapies in cancer and beyond. SignificanceMajor efforts are underway to identify cancer-specific antigens for personalized immunotherapy. Here, we enrich the immunopeptidome landscape by uncovering thousands of novel putative antigens that are post-translationally modified. We define unique preferences for PTM-driven alterations affecting HLA binding and TCR recognition, which in turn alter tumor-immune interactions. Conflict of interest statementAuthors declare no conflicts of interest.

cancer biology↗

bioRxiv2021

High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe

The dia-PASEF technology exploits ion mobility separation for high-sensitivity analysis of complex proteomes. Here, we demonstrate neural network-based processing of the ion mobility data, which we implement in the DIA-NN software suite. Using spectral libraries generated with the MSFragger-based FragPipe computational platform, the DIA-NN analysis of dia-PASEF raw data increases the proteomic depth by up to 69% compared to the originally published dia-PASEF workflow. For example, we quantify over 5200 proteins from 10ng of HeLa peptides separated with a 95-minute nanoflow gradient, and over 5000 proteins from 200ng using a 4.8-minute separation with an Evosep One system. In complex samples, featuring a mix of human and yeast lysates, the workflow detects over 11700 proteins in single runs acquired with a 100-minute nanoflow gradient, while demonstrating quantitative precision. Hence, the combination of FragPipe and DIA-NN provides a simple-to-use software platform for dia-PASEF data analysis, yielding significant gains in high-sensitivity proteomics.

bioinformatics↗

bioRxiv2021

Viperin inhibits cholesterol biosynthesis and interacts with enzymes in the cholesterol biosynthetic pathway

Many enveloped viruses bud from cholesterol-rich lipid rafts on the cell membrane. Depleting cellular cholesterol impedes this process and results in viral particles with reduced viability. Viperin (virus inhibitory protein endoplasmic reticulum-associated, interferon-induced) is an ER membrane-associated enzyme that when expressed in response to viral infections exerts broad-ranging antiviral effects, including inhibiting the budding of some enveloped viruses. Here we have investigated the effect of viperin expression on cholesterol biosynthesis. We found that viperin expression reduces cholesterol levels by 20 - 30 % in HEK293T cells. A proteomic screen of the viperin interactome identified several cholesterol biosynthetic enzymes among the top hits. The two most highly enriched proteins were lanosterol synthase and squalene monooxygenase, enzymes that catalyze key steps establishing the sterol carbon skeleton. Co-immunoprecipitation experiments established that viperin, lanosterol synthase and squalene monooxygenase form a complex at the ER membrane. Co-expression of viperin was found to significantly inhibit the specific activity of lanosterol synthase in HEK293T cell lysates. Co-expression of viperin had no effect on the specific activity of squalene monooxygenase, but reduced its expression levels in the cells by approximately 30 %. Despite these inhibitory effects, co-expression of either LS or SM failed to reverse the viperin-induced depletion of cellular cholesterol levels in HEK293T cells. Our results establish a clear link between the down-regulation of cholesterol biosynthesis and viperin, although at this point the effect cannot be unambiguously attributed interactions between viperin and a specific biosynthetic enzyme.

biochemistry↗

bioRxiv2020

Label-free quantification with FDR-controlled match-between-runs

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared to MaxQuant (19-38 times faster; 6-18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant, and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry (FAIMS) separation. Fully integrated in FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.

bioinformatics↗

bioRxiv2020

Light-mediated discovery of surfaceome nanoscale organization and intercellular receptor interaction networks

Delineating the molecular nanoscale organization of the surfaceome is pre-requisite for understanding cellular signaling. Technologies for mapping the spatial relationships of cell surface receptors and their extracellular signaling synapses would open up theranostic opportunities and the possibility to engineer extracellular signaling. Here, we developed an optoproteomic technology termed LUX-MS that exploits singlet oxygen generators (SOG) for the light-triggered identification of acute protein interactions on living cells. Using SOG-coupled antibodies, small molecule-drugs, biologics and intact viral particles, we show that not only ligand-receptor interactions can be decoded across organisms, but also the surfaceome receptor nanoscale organization ligands engage in with direct implications for drug action. Furthermore, investigation of functional immunosynapses revealed that intercellular signaling inbetween APCs and CD8+ T cells can be mapped now providing insights into T cell activation with spatiotemporal resolution. LUX-MS based decoding of surfaceome signaling architectures provides unprecedented molecular insights for the rational development of theranostic strategies.

systems biology↗

bioRxiv2020

Fast and Comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Glycosylation is a ubiquitous and heterogeneous post-translational modification (PTM) used to accomplish a wide variety of critical cellular tasks. Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale, high-quality glycoproteomics datasets, but interpreting this data remains challenging. In addition to being large, complex, and heterogeneous, glycans undergo fragmentation during vibrational activation, making common PTM search strategies ineffective for their identification. We present a computational tool called MSFragger-Glyco for fast and highly sensitive identification of N- and O-linked glycopeptides using open and glycan mass offset search strategies. Reanalysis of recently published N-glycoproteomics data resulted in annotation of 83% more glycopeptide-spectrum matches (glycoPSMs) than in previous results, which translated to substantial increases in the numbers of glycoproteins and glycosites that could be identified. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same peptides as the original search and resulted in up to a 6-fold increase when expanding searches to include large numbers of possible glycan compositions and other modifications. Expanded searches revealed trends in glycan composition and crosstalk with phosphorylation that remained hidden to the original search. With greatly improved spectral annotation, coupled with the fast speed of fragment ion index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

bioinformatics↗

bioRxiv2020

Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant

Ion mobility brings an additional dimension of separation to liquid chromatography-mass spectrometry, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to time-of-flight mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most commonly used to analyze these data. However, due to the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 minutes on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation prior to MS/MS analysis.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsLC-MSliquid chromatography-mass spectrometryTIMStrapped ion mobility spectrometryTOFtime-of-flightPASEFparallel accumulation-serial fragmentationDDAdata-dependent acquisitionDIAdata-independent acquisitionMS/MStandem mass spectrometryPSMpeptide-spectrum matchLC-IMS-MSliquid chromatography-ion mobility-mass spectrometryXICextracted ion chromatogramCVcoefficient of variationLFQlabel free quantificationFDRFalse Discovery RateCIDcollision-induced dissociationCPUcentral processing unitPTMpost-translational modificationMBRmatch-between-runsView Full Text

bioinformatics↗

bioRxiv2018

LiF-MS: Mapping unstructured peptide-protein interactions using Ligand-Footprinting Mass Spectrometry

Unstructured peptides, or linear motifs, present a poorly understood molecular language within the context of cellular signaling. These modular regions are often short, unstructured and interact weakly and transiently with folded target proteins. Thus, they are difficult to study with conventional structural biology methods. We present Ligand-Footprinting Mass Spectrometry, or LiF-MS, as a method of mapping the binding sites and dynamic disorder of these peptides on folded protein domains. LiF-MS uses a cleavable crosslinker to mark regions of a protein contacted by a bound linear motif. We demonstrate this method can detect both conformation ensembles and binding orientations of a linear motif in its binding pocket to amino-acid-level detail. Furthermore, marked amino acids can be used as constraints in peptide-protein docking simulations to improve model quality. In conclusion, LiF-MS proves a simple and novel method of elucidating peptide docking structural data not accessible by other methods in the context of a purified system.

biochemistry↗

bioRxiv2017

PIQED: Automated Identification And Quantification Of Protein Modifications From DIA-MS Data

Label-free quantification using data-independent acquisition (DIA) is a robust method for deep and accurate proteome quantification1,2. However, when lacking a pre-existing spectral library, as is often the case with studies of novel post-translational modifications (PTMs), samples are typically analyzed several times: one or more data dependent acquisitions (DDA) are used to generate a spectral library followed by DIA for quantification. This type of multi-injection analysis results in significant cost with regard to sample consumption and instrument time for each new PTM study, and may not be possible when sample amount is limiting and/or studies require a large number of biological replicates. Recently developed software (e.g. DIA-Umpire) has enabled combined peptide identification and quantification from a data-independent acquisition without any pre-existing spectral library3,4. Still, these tools are designed for protein level quantification. Here we demonstrate a software tool and workflow that extends DIA-Umpire to allow automated identification and quantification of PTM peptides from DIA. We accomplish this using a custom, open-source graphical user interface DIA-Pipe (https://github.com/jgmeyerucsd/PIQEDia/releases/tag/v0.1.2) (figure 1a).\n\nO_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC=\"FIGDIR/small/141382_fig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@41b8f3org.highwire.dtl.DTLVardef@d59733org.highwire.dtl.DTLVardef@b9a722org.highwire.dtl.DTLVardef@8be50c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Automated Qualitative and Quantitative Analysis of Post-Translational Modifications using DIA-Pipe. (a) Workflow of the all-DIA strategy for identification and quantification of PTMs. Modified peptides enriched from biological samples are analyzed by data-independent acquisition. All data analysis steps starting from instrument. wiff files, acquired on a TripleTOF 5600, can be completed using the DIA-Pipe GUI, including: (1) file conversion and pseudo-MS/MS spectra generation using DIA-Umpire, (2) database searching by MS-GF+, X! Tandem, and Comet followed by results refinement and combination using PeptideProphet/iProphet/PTMProphet, (3) automated spectral library generation and fragment area extraction using SkylineRunner, and finally, (4) Skyline report filtering and formatting for significance testing with mapDIA. (b) mProphet composite score distributions of target and second-best peaks picked by Skylineshowing essentially error-free peak picking by Skyline of peptides identified using pseudo-MS/MS spectra. (c) Distribution of coefficient of variations observed from three technical replicates for 1,182 acetylation sites. (d) Observed distributions of log2(fold change) computed by mapDIA using three technical replicates of 1X injection volume compared to three technical replicates of 0.5X injection volume; expected log2(fold change) value =1. (e) Number of identified peptides from each single replicate injection and from the combination of two or three replicates. An average of 1,655 acetylated peptides were identified per replicate. The combination of two or three replicates increased the number of identifications by 19% or 29%, respectively.\n\nC_FIG

systems biology↗